SoroushVahidi/scidocs huggingface dataset:
Measuring Machine Intelligence Through Visual Question Answering
(2016)

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.
Issues in User Authentication 1
(2007)

CHI 2007 Workshop: Security user studies: methodol gies and best practices, April 2007. ABSTRACT In this brief paper we outline our recent work on u sability studies of user authentication systems. In particul ar, we have conducted studies on password managers and graphical password systems, using in-lab experiment s, interviews, and broader field studies. We then disc us some of the important issues arising, including mental m odels of security, the role of persuasion, and the nature of identify theft.
Network Traffic Management
(2009)

The purposes of this paper have to discuss issues related to Network Traffic Management. A relatively new category of network management is fast becoming a necessity in converged business Networks. Mid-sized and large organizations are finding they must control network traffic behavior to assure that their strategic applications always get the resources they need to perform optimally. Controlling network traffic requires limiting bandwidth to certain applications, guaranteeing minimum bandwidth to others, and marking traffic with high or low priorities. This exercise is called Network Traffic Management.
Internal Uniplanar Antenna for LTE/GSM/UMTS Operation in a Tablet Computer
(2013)

With the use of an internal printed loop matching circuit, a small-size uniplanar antenna with eight-band LTE/GSM/UMTS operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 262/1610 MHz for the LTE/GSM/UMTS bands, respectively. Only with the antenna size of $40\times 15\times 0.8\ {\rm mm}^{3}$, the proposed planar antenna has the compact operation with more than 30% antenna size reduction. Furthermore, the measured peak gains and antenna efficiencies are about 2.81/4.97 dBi and 79/87% for the LTE/GSM/UMTS bands, respectively.
Novice Programmers and the Problem Description Effect
(2016)

It is often debated whether a problem presented in a straightforward minimalist fashion is better, or worse, for learning than the same problem presented with a "real-life" or "concrete" context. The presentation, contextualization, or "problem description" has been well studied over several decades in disciplines such as mathematics education and psychology; however, little has been published in the field of computing education. In psychology it has been found that not only the presence of context, but the type of context can have dramatic results on problem success. In mathematics education it has been demonstrated that there are non-mathematical factors in problem presentation that can affect success in solving the problem and learning. The contextual background of a problem can also impact cognitive load, which should be considered when evaluating the effects of context. Further, it has been found that regarding cognitive load, computer science has unique characteristics compared to other disciplines, with the consequence that results from other disciplines may not apply to computer science, thus requiring investigation within computer science.
 This paper presents a multi-national, multi-institutional study of the effects of problem contextualization on novice programmer success in a typical CS1 exercise.
A PVT-insensitive CMOS output driver with constant slew rate
(2004)

In this paper, we propose an output driver which has constant slew rate over PVT variation. To make output driver's slew rate to be constant, rising and failing time of pre-driving node of output driver is kept constant by predriver, which has constant pull-up and pull-down path resistance. To make output voltage swing level to be constant, another replica bias circuit was used with CMOS push-pull output driver. HSPICE simulations show that the slew rate is 3.21 V/nS on fast condition and 3.12V/nS on slow condition with 1.4K pre-driver's path resistance. This circuit was implemented with 0.18 /spl mu/m CMOS process.
Sensorless vector control of asynchronous machine based on reduced order Kalman filter
(2018)

The article describes sensorless vector control of the asynchronous machine based on Kalman filter. A control system block diagram is presented along with a functional scheme of the original speed observer using dynamic system state vector filter estimator. The synthesis of current regulator using machine parameters determined by inverse r model is shown. The method of a control matrix dimension reduction is proposed by removal the flux linkage calculations out of Kalman filter algorithms. Current and speed regulators, as well as a flux estimator, were implemented in the Mexbios Development Studio simulation environment. The robustness of a speed observer and the efficiency of the asynchronous machine sensorless control system were confirmed by the simulation results.
Rapid solution of problems by nuclear-magnetic resonance quantum computation
(2000)

We offer an improved method for using a nuclear-magnetic-resonance quantum computer (NMRQC) to solve the Deutsch-Jozsa problem. Two known obstacles to the application of the NMRQC are exponential diminishment of density-matrix elements with the number of bits, threatening weak signal levels, and the high cost of preparing a suitable starting state. A third obstacle is a heretofore unnoticed restriction on measurement operators available for use by an NMRQC. Variations on the function classes of the Deutsch-Jozsa problem are introduced, both to extend the range of problems advantageous for quantum computation and to escape all three obstacles to use of an NMRQC. By adapting it to one such function class, the Deutsch-Jozsa problem is made solvable without exponential loss of signal. The method involves an extra work bit and a polynomially more involved Oracle; it uses the thermal-equilibrium density matrix systematically for an arbitrary number of spins, thereby avoiding both the preparation of a pseudopure state and temporal averaging.
3D Face Recognition using Kinect
(2014)

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
(2003)

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels
(2018)

The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.
MODEL DIFFERENCES IN THE ECLIPSE MODELING FRAMEWORK
(2008)

Increasingly, recording the various kinds of design-level structural evolution that a system undergoes throughout its entire life-cycle is gaining a fundamental importance and cannot be neglected in software modeling and development. In this respect, an interesting and useful operation between the designs of subsequent system versions is the difference management consisting in calculation, representation, and visualization. This work presents EMF Compare, an approach to model difference calculation and representation for the Eclipse modeling framework. Apart from leveraging the rank of model differences to that of first-class artifacts according to the “everything is a model” principle, the approach presents several properties which are discussed according to a conceptual framework.
A Blended Deep Learning Approach for Predicting User Intended Actions
(2018)

User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.
OINT C LOUDS
(2018)

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep autoencoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable basic shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation. We perform a thorough study of different generative models including: GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs and, Gaussian mixture models (GMM). For our quantitative evaluation we propose measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our careful evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs produce the best results.
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
(2017)

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
Enterprise systems implementation and accounting benefits
(2006)

Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption. Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices. Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules. Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.
Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction
(2015)

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.
Secure Logging as a Service—Delegating Log Management to the Cloud
(2013)

Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.
Revenue : 229 B Revenue : ? Birth Year : ? Height : ? Weight : 70
(2018)

Knowledge bases (KB) are often represented as a collection of facts in the form (head, predicate, tail), where head and tail are entities while predicate is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (Paris, latitude, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of Freebase and Yago show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.
Severely suppressed bone turnover: a potential complication of alendronate therapy.
(2005)

Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.
Risk factors for shoulder and elbow injuries in adolescent baseball pitchers.
(2006)

BACKGROUND
There is little evidence supporting current safety recommendations for adolescent pitchers.


HYPOTHESIS
Pitching practices of adolescent pitchers without history of arm injury will be significantly different from those of adolescent pitchers who required shoulder or elbow surgery.


STUDY DESIGN
Case control study; Level of evidence, 3.


METHODS
Ninety-five adolescent pitchers who had shoulder or elbow surgery and 45 adolescent pitchers who never had a significant pitching-related injury completed a survey. Responses were compared between the 2 groups using t tests and chi(2) analyses. Multivariable logistic regression models were developed to identify the risk factors.


RESULTS
The injured group pitched significantly more months per year, games per year, innings per game, pitches per game, pitches per year, and warm-up pitches before a game. These pitchers were more frequently starting pitchers, pitched in more showcases, pitched with higher velocity, and pitched more often with arm pain and fatigue. They also used anti-inflammatory drugs and ice more frequently to prevent an injury. Although the groups were age matched, the injured group was taller and heavier. There were no significant differences regarding private pitching instruction, coach's chief concern, pitcher's self-rating, exercise programs, stretching practices, relieving frequency, pitch type frequency, or age at which pitch types were first thrown.


CONCLUSION
Pitching practices were significantly different between the groups. The factors with the strongest associations with injury were overuse and fatigue. High pitch velocity and participation in showcases were also associated with increased risk for injury.


CLINICAL RELEVANCE
New recommendations were made based on these results. Adherence to the recommendations may reduce the incidence of significant injury to adolescent pitchers.
Christopher Alexander's fifteen properties: Toward developing evaluation metrics for security visualizations
(2013)

The objective of security data visualizations is to help cyber analysts to perceive trends and patterns, and gain insights into security data. Sound and systematic evaluations of security data visualizations are rarely performed, in part due to the lack of proper quantitative and qualitative measures. In this paper, we present a novel evaluation approach for security visualization based on Christopher Alexander's fifteen properties of living structures. Alexander's fifteen properties are derived from various visual patterns that appear in nature. Each property represents the guidelines for good design. We believe that using these fundamental properties have the potential for building a more robust evaluation. Each property offers essential qualities that enable better analytical reasoning. We demonstrate how to use Alexander's properties to evaluate security related visualizations. We derive a set of visualization-specific properties for evaluation, developed based on Alexander's original properties.